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IT Strategies for Asset Management of Substations -

General Principles

Working Group B3.06 TF05

April 2014

IT STRATEGIES FOR ASSET

MANAGEMENT OF SUBSTATIONS -

GENERAL PRINCIPLES WG B3.06 TF05

Members

J. Smit, Convenor (NL), N. Barrera, Secretary (CH),

Alan Wilson (UK), Nicolaie L. Fantana (DE), Jan Bednarik (IRL), Ravish Mehairjan(NL), Paul Leemans

(BE), Iliana Portugues (UK), Paul Myrda (USA), Gerd Balzer (DE), Jan Bruinenberg (NL), Gilles

Buffiére (FR), Paulino Aparicio (ES), Bente Bakka (NO), Hylco E. Hoekstra (NL), Serge Laederach,

TF05 convener (CH), Thomas Melkersson (SE), Arthur Mackrell (UK), Tatsuru Kobayashi (JP), Qikai

Zhuang(NL), Zhao Ma (UK), Ferenc Bodrogi (HU), Petr Spurný (CZ), Cawir Ginting (IDN), Dagmar

Kopejtková (CZ), Ph. Wester+ (NL)

Copyright ©

“Ownership of a CIGRE publication, whether in paper form or on electronic support only infers right of use for personal purposes. Are prohibited, except if explicitly agreed by CIGRE, total or partial reproduction of the publication for use other than personal and transfer to a third party; hence circulation on any intranet or other company network is forbidden”.

Disclaimer notice

“CIGRE gives no warranty or assurance about the contents of this publication, nor does it accept any responsibility, as to the accuracy or exhaustiveness of the information. All implied warranties and conditions are excluded to the maximum extent permitted by law”.

ISBN: 978-285-87327-15

IT STRATEGIES FOR ASSET MANAGEMENT OF SUBSTATIONS - GENERAL PRINCIPLES

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ISBN: 978-285-87327-15


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IT STRATEGIES FOR ASSET MANAGEMENT

OF SUBSTATIONS - GENERAL PRINCIPLES


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Table of Contents

EXECUTIVE SUMMARY ........................................................................................... 7

INTRODUCTION ................................................................................................. 12

IT STRATEGIES FOR ASSET MANAGEMENT OF SUBSTATIONS - GENERAL PRINCIPLES13

Information strategy to support utility asset management .............................. 13

Decision making with regard to stakeholder oriented risk management ......... 14

Data management – Intelligent hub ................................................................ 14

Survey on Practical Application of Asset Management Information Strategies . 15

Conclusions .................................................................................................... 16

INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT .............. 17

Asset Management Functional Model .............................................................. 18

Asset Management Processes ......................................................................... 19

Step 1: Determine/adjust vision and goals ................................................... 20

Step 2: Determine/adjust strategy and policy ............................................... 21

Step 3: Planning ............................................................................................ 21

Step 4: Realisation ........................................................................................ 21

Decision Process ............................................................................................. 22

Risk/condition assessment ........................................................................... 23

Decision flow ................................................................................................ 23

Information Requirements .............................................................................. 25

Data Categories .............................................................................................. 26

Technical data category ................................................................................ 27

Economic data category ................................................................................ 27

Social data category ...................................................................................... 28

Information Pyramid ....................................................................................... 28

“Front” information systems – data input layer.............................................. 29

Data warehouse – data integration layer ....................................................... 29

Analysis tools – data analysis layer ............................................................... 30

Decision support – decision layer ................................................................. 30


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Conclusions .............................................................................................. 31

IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT

......................................................................................................................... 32

Decision making with regard to stakeholder oriented risk management ... 33

Stakeholder orientation .......................................................................... 33

Risk balancing using a scenario approach............................................... 34

The intelligent hub ................................................................................. 36

Asset oriented model .............................................................................. 37

Dimensional model ................................................................................. 39

Processing data ...................................................................................... 40

The architectural overview ...................................................................... 40

Conclusions and further outlook .............................................................. 42

IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT

- DECISION MAKING WITH REGARD TO STAKEHOLDER ORIENTED RISK MANAGEMENT

......................................................................................................................... 44

Decision making with regard to stakeholder oriented risk management ... 45

The decision process considering various scenarios ............................... 45

Requirements ........................................................................................... 50

Stakeholder Requirements ...................................................................... 50

Information requirements ....................................................................... 52

Key performance indicators (KPI’s) .......................................................... 55

Combining different KPI’s (indexing) ...................................................... 57

Conclusion ............................................................................................... 58

SURVEY ON PRACTICAL APPLICATION OF ASSET MANAGEMENT INFORMATION STRATEGIES

......................................................................................................................... 59

Experiences .............................................................................................. 59

Access to data and conversion into information ....................................... 60

Software tools to support decision making ............................................... 60

Data Sources ............................................................................................. 61

Recording of primary and secondary equipment ..................................... 62


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Outage, performance and failure data .................................................... 63

Maintenance and repair data................................................................... 64

Diagnosis and Monitoring data ............................................................... 65

Data collection and systems used ........................................................... 66

Data processing and data warehousing .................................................. 68

Systems in use ........................................................................................ 69

Data processing from source to data warehouse .................................... 71

Synchronizing of data ............................................................................. 71

Data quality and validation ..................................................................... 72

Data requirements .................................................................................. 72

Information requirements ....................................................................... 74

Scenario approach and risk balancing ....................................................... 75

Strategy, depending on the type of equipment ....................................... 77

Applied philosophy for maintenance strategy, depending on the type of equipment

............................................................................................................... 79

Use of risk assessment ........................................................................... 81

Decision and control ................................................................................. 82

Decision Policy based on IT support systems .......................................... 83

Economic criteria .................................................................................... 84

Technical criteria .................................................................................... 84

Social criteria .......................................................................................... 85

Risk criteria ............................................................................................ 86

Conclusion ............................................................................................... 87

FINAL CONCLUSIONS .......................................................................................... 88

REFERENCES ....................................................................................................... 92


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EXECUTIVE SUMMARY

Background of the Technical Brochure

The importance of establishing a well-considered information strategy within transmission

and distribution utilities as a basis for decision making was recognized early by CIGRE. In

the year 2000 the Joint Working Group (JWG) B3/C2-14/15 was requested to initiate the

work regarding information strategy. During the period following the commencement of

the JWG, utility organisational structures have changed immensely resulting from the

liberalisation and separation of independent market and system operators (hence the

coming into existence of the asset manager/ service provider business environment).

Therefore, the work of the JWG, which continued and was finalized by WG B3-06, has

concentrated on the information strategy as applicable in one of the separate functions of

a utility. This Technical Brochure (TB) describes an information strategy pertinent to the

asset management function with emphasis on substation asset management.

Introduction

According to CIGRE WG C1-01, asset management involves the centralisation of key

decision-making for the network business to maximise long term profits, whilst delivering

high service levels to customers, with acceptable and manageable risk. In this context, risk

management is seen as a mainstream regime to enable asset managers to translate

corporate business values and requirements into a comparable, measurable and

manageable dimension, namely Risk. Although the concepts of asset management have

existed for over twenty years, utility asset managers still need to settle for a less than ideal

condition regarding asset information to support risk-based decision making. This TB

shows that this is mainly due to the on-going strong focus on technical data and less on

economic and social data. Risk-based decision making, however, requires data in mixed

strategies and matching technical, economic and social requirements from a holistic point

of view. Succeeding in filling this gap is the first step in obtaining better decisions in the

field of asset management.

The work of JWG B3/C2-14 followed by WG B3-06 instigates in filling the gap between

data management, through information strategies, and the link with risk based decision


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making. This TB establishes general principles for the information strategy for asset

management and in particular, for substation asset management.

Description of the TB

In chapter 1, a brief introduction is given of this TB describing the reason for initiating a

joint working group between the substation and system committees to consider the topic

“Information Strategies” from an asset management point of view. The contents of the

remaining chapters are explained as well as the contributions which were derived from

each subsequent chapter.

Chapter 2 starts with the “hourglass model”. A model which is described in this TB is as an

information strategy that, essentially, consists of two parts. These are:

The risk management in a business relevant environment: this helps in utilizing and

addressing the requirements on asset data

The data management: this supports the decision making process through

constructing a system to acquire, warehouse and transmit data.

The linkage of the above mentioned two parts is the critical connection. This connection,

named “intelligent hub” in this TB, aligns the translation of the information requirements

from the “risk management side” to the requirements understandable by the “data

management” side. In the remainder of the TB this conceptual “hourglass model” will be

used to describe the information strategy process.

With the goal to determine the status of current information strategy practices, an

international survey was carried out. The survey comprises 19 utilities from Europe, North

America, Middle East and Australia. The main results are highlighted in chapter 2, showing

that within utilities the focus of data management is more on the technical level and less

on economic and social data. While, a multi criteria decision making process needs

matching technical, economic and social data.

In chapter 3 the role of information strategy within asset management is defined. This

asset management regime is based on the generic asset management functional model,

which contains three domains, namely operation, maintenance and management. These

domains are used to structure the asset management decision process. Moreover,

decisions are supported by information in three categories, which are:


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the technical category, which specify the condition for individual component and

offers solution scenarios;

the economic category, which assesses the benefit of technical scenarios on

reliability;

the societal category, which assesses the benefit of technical scenarios on risks and

make the final decision.

The “information pyramid” is presented and describes a framework to realize the multilevel

and categorized information model, which can be used to support decision processes in a

stakeholder oriented risk management regime.

In chapter 4, a practical approach of implementing an asset management information

strategy was outlined using the “hourglass model” to explain the transition from risk

management into data management. The value of the different stakeholder groups are

used for the process of risk management, which is in line with stakeholders needs with

regard to the three earlier mentioned information categories. Furthermore, the “intelligent

hub” is utilized to explain methods and approaches to transform information requirements

into data requirements. This information process can be regarded as one of the most

important features for asset managers. In relation to the requirements of data, the

processing methods (data extraction from existing data bases), usually referred to data

warehousing, is described. The described information process has an iterative character

with a lead line coming from the business relevance instead of, nowadays still often

feasible, technical relevance.

An information strategy focussed on stakeholder oriented risk management is described in

chapter 3. In chapter 4 a data architecture design applicable for substation replacements is

given. Chapter 5 makes the link by describing an implementation of an information

strategy to support utility asset management. This chapter firstly expands the decision

making process of chapter 3 into a complete information flow model. The model

enumerates the available technical, economic and societal information source at

component, network and corporate level, respectively. Based on a set of simplified

scenarios of substation replacement, a detailed key performance indicator (KPI) dictionary

is given as a general guideline for further design of database and software.


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The content explained through chapters 3 till 5 is considered to be in advance of the

practice of today’s utility companies. Hence, the necessity is to reveal the implementation

requirements of information strategies within utilities. For this purpose, as briefly

highlighted in chapter 2, the WG distributed an international survey in the year 2006/2007

to get more information about the applied asset strategies and their related data

management. The questionnaire, of which the detailed results are discussed in chapter 6,

was focused on elucidating the current experiences, practices, support of information

technology tools, and applied data management methodologies. In general, the results

indicate that a majority of utilities own a large volume of digital records regarding daily

operation and maintenance on a wide range of substations components. However, when it

comes down to data processing the focus is mainly on technical aspects. Consequently,

decisions on maintenance strategies are much better supported than those on

replacement.

In chapter 7 comprehensive final conclusions are drawn. Overall, the TB presents an

overview of the methodology used for defining IT-strategies for AM and generic guidelines

for initiating information strategies. In general, based on the study and survey of the WG,

the following conclusions are drawn:

The decision making process needs a holistic approach in the field of replacement,

renewal and maintenance strategies. The results indicate that mixed strategies are

common in most of the utilities, depending on the type of the components.

By following the “hourglass model”, which is described in this report, the “right

information” should be extracted from the huge amount of available data with the

focus to support asset management decisions.

The survey results indicate the remarkable need for enterprise resource planning

(ERP) systems in the utilities, with the purpose of integrating the existing raw, or

rather unprocessed data.

Accordingly, the “hourglass model” shows that there is a need to link information

requirements with data requirements. In order to obtain such a link, it is necessary

to have integrated data containing technical, economic, social information, and a

well-defined enterprise-wide data model.


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Technical data is found to stand supreme in comparison to economic and social data in

existing models at utilities. But then again, the decision making processes require data

models that describe data needed in mixed strategies, and match technical, economic and

social requirements from a holistic approach. Succeeding in closing this gap is a

prerequisite for obtaining superior decisions in the field of asset management.


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INTRODUCTION

The importance of asset management information strategies was recognized early in

CIGRÉ. An initial working group was started as joint work between substation and systems

committees in the year 2000 to consider “Information Strategies” from the asset

management point of view. This work was then continued in WG B3-06 on Substation

management.

In a first step the asset management business, the decision process and the information

requirements have been analysed. The results of this work are published in a scoping

paper in Electra which is reproduced in Chapter 3.

In a second step the study went more into details. Stakeholder orientation and business

relevance were leading to a risk management approach which balances economic,

technical and societal aspects. Also important issues of work were the data management

to allocate and to share raw data and the intelligence to translate data into the necessary

information. A summary of this work has been published during the A3 & B3 joint

colloquium 2005 in Japan and it is reproduced in Chapter 4 [1].

Additional studies were done to consider in more detail the business relevance for risk

based asset management decision making process as well as for information requirements

and data modelling. The results of these studies are given in Chapter 5.

During the process it became clear, that the work should be followed by investigations into

the present status of application practice. These investigations focused on skills and tools

as applied in utilities. A survey was initiated with the task to report about practical

application of asset management information strategies. The survey results are presented

in Chapter 6.

The brochure closes with a conclusion and an outlook in Chapter 7.


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IT STRATEGIES FOR ASSET MANAGEMENT OF SUBSTATIONS -

GENERAL PRINCIPLES

Information strategy to support utility asset management

The information strategy is based upon a functional model. This model describes the

different accountabilities of the asset management function. A generic data model

illustrating the various items and categories of data is presented as well as information

architecture to support asset management decision making. It was concluded that all asset

management decisions should cover both technical and economic consequences of

decisions as well as the potential societal and strategic impacts. Models for the decision

processes, requirements and data categories show the complexity of the process.

As asset management is a synonym for risk management a model “from risk to data

management” (Fig. 1) is used. It recognizes the essential differences between: risk

management in a business relevant environment and data management support for the

decision making process. The model expresses the need to align the translation of data

into information according to the risk based decision process. This process takes into

account all relevant areas as social, technical and economic.

Following this “hourglass” model, setting a business point of view will be described in

Chapter 4.


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Figure 1: From risk to data management

Decision making with regard to stakeholder oriented risk

management

The key success factor for electricity supply in general and for an asset manager is to

satisfy all stakeholder requirements. As stakeholders' interests are different and possibly

conflicting the asset management decisions are always the result of a trade-off. A

sustainable asset management process has therefore to consider technical, economic as

well as social and environmental aspects and must find a balance between all stakeholder

values and requirements. Risk management is seen as one of the most important

instruments of asset management as it enables the asset manager to translate those

values and requirements into one comparable dimension: risk.

Data management – Intelligent hub

In the data management part of the brochure the “intelligent hub” is described. It shows

methods and approaches to transform information requirement into data requirements.

This transformation process can be regarded as one of the most important features for

asset managers. Related to the data requirements data processing methods (data

warehousing) are described that provide data extraction from existing data bases. A

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description of the specific data sources for this data processing concludes the data

management chapters. It is emphasized that the described process has an iterative

character and it is mainly driven by the business relevance and not by the technical

relevance.

Survey on Practical Application of Asset Management Information

Strategies

The goal of the survey was to determine the status of current practices, how and which IT-

tools can support asset management and which methodologies are being used. The survey

comprises the results from 19 utilities from Europe, North America, Middle East and

Australia. The main results are highlighted in the following:

About integration: The decision making process in the field of replacement/renewal

and maintenance strategies needs a holistic approach. The results show that the

majority of the utilities use mixed strategies depending on the type of equipment.

Decisions are mostly based on both technical and economic criteria.

About data: Primary equipment is mostly stored in enterprise-wide databases, but

this is less common with secondary equipment. The information is mostly recorded

on asset level. Due to its importance failure, outage, performance and maintenance

data is recorded by most companies. In addition there is a high degree of recording

diagnosis (offline) data. Most of this data is still being collected by hand.

About IT-systems: Utilities have a remarkable need for enterprise resource planning

systems (ERP) and the integration of basic data. Integration of technical systems

(e.g. SCADA, GeoIS, EAM) and non-technical systems (e.g. CRM, ERP) are not

common in the branch. Asset management strategies identify the need for data

integration and results show that many utilities are only at the beginning of this

integration, starting to buy and to implement enterprise-wide systems and

introducing solutions such as data warehousing as an attempt to integrate data.

About modelling: The survey shows that enterprise-wide data models do exist.

Nevertheless the survey shows that data models from source systems are not

sufficient in the asset managers' need of a holistic modelling approach.


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Summing up, results show that existing models within the utilities themselves remain

strongly focus on technical data and less on economic data. Social data is only present in

some models. The decision-making process on the other hand requires data models

describing data in mixed strategies and matching technical, economic and social

requirements from a holistic point of view. Succeeding in filling this gap is the first step in

obtaining better asset management decisions.

Conclusions

Based on analysis of the total asset management process, the requirements of the different

stakeholders need to be considered. In this connection it makes sense to define various

key performance indicators (KPI) depending on defined scenarios and to perform a risk

assessment. This procedure requires a significant amount of data with suitable quality.

This data needs to be up to date, an appropriate data model should be used and

furthermore a master data set has to be maintained. Finally the survey provides an

overview of currently used strategies and information systems in the area of power supply

companies.


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INFORMATION STRATEGY TO SUPPORT UTILITY ASSET

MANAGEMENT

In 2000 the JWG B3/C2-14, covering the area of maintenance and reliability, was

requested to begin work regarding Information Strategy for utilities. A task force was

established based on a terms of reference document [2]. These terms mentioned the

extreme strategic importance of setting up a well-considered Information Strategy within

utilities as a basis for decision making in the application of Information Systems. The

Information Strategy should focus on how utilities hold and manage data and information

on their facilities. Decisions that may involve substantial investment in Information

Technology must be based on an analysis of the business processes of the utility and its

needs.

During the period following the initiation of the work mentioned above, liberalisation of

utility processes has continued and this has had a major influence on utilities’

organisational structures. As such, a separation between independent market and system

operators and asset managers and service providers has been recognised. The separation

between these functions, even to the level of complete independent corporations, has

directed the work of the task force to concentrate on the Information Strategy as

applicable in one of the separated utilities’ functions. This paper describes an information

strategy applicable to the asset management function. Service provider functions will

generally focus on satisfying the information needs of the asset management function.

According to the Cigré Working Group C1-01 on Asset Management 3, with which

agreement this work is published, the asset management responsibilities of a transmission

or distribution business operating in an electricity market involves the centralisation of key

decision making for the network business to maximise long term profits, whilst delivering

high service levels to customers, with acceptable and manageable risks.

The asset manager’s processes should guarantee the satisfaction of the stakeholders (e.g.

customers, shareholders, employees, the assets owners, asset operators and the


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regulator), who will be benchmarking performance against other utilities 3, 4. The asset

manager’s function can thus be placed in context and further details are explained in

Chapter 3.

Asset Management Functional Model

Organisational differences and differing asset management philosophies and priorities

have resulted in significant disparity across different companies with respect to asset

management strategy and implementation.

This diversity makes it difficult to analyse and compare asset management

implementations and their relative efficiencies. Therefore, to support the understanding of

asset management implementations, a generic functional model for asset management,

illustrated in Fig. 2, as well as a description of the asset management accountabilities,

shown in Fig. 3, is given.

Figure 2: Functional model for asset management

The purpose of the functional model is to present a structured functional approach to the

asset management process. The accountabilities are described to form a basis for the

investigation of information requirements for particular units within a utility. Chapter 2.4


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explains the procedures and associated data/information requirements related to the main

asset management process steps:

Vision and goals,

Strategies, policies, standards,

Planning,

Implementation, realization.

Figure 3: Overview of accountabilities

Asset Management Processes

A comprehensive understanding of the asset management process is important in defining

data/information requirements and a consequent strategy. A model for asset management

processes was developed that reflects responsibilities regarding optimisation of decisions.


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The asset management function must be flexible enough to respond to the changing

demands and expectations of stakeholders, regulators/legislators and customers.

Accordingly, an iterative and evolving process must be defined as illustrated in Fig. 4. This

indicates where and how data and information relating to the process is used within each

of the main process steps.

Figure 4: Asset management process - main functional steps and data/information

requirements

Step 1: Determine/adjust vision and goals

Based on the requirements and objectives of the main stakeholders, regulators/legislators

and clients, the organisation determines its vision and goals. This is normally initially

achieved via a business plan, which is subsequently reviewed on an iterative basis.


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Adjustments are often made based on changing demands and feedback from the

organisation’s performance.

Step 2: Determine/adjust strategy and policy

The next step in the process is to determine the organisation’s strategy and policy. This is

based on environmental and regulatory scenarios. The use of scenarios will give the

necessary flexibility. Per scenario the expectations which are related to the different

aspects of the asset management function, are stated and answered by actions and

reactions; thus stating policies. The policies (e.g. investment in LV, MV or HV networks,

application of FACTS devices etc.) that facilitate the majority of scenarios (e.g.

(de)centralised generation) should be prioritised. Following on from strategy definition, it

is necessary to translate the strategy into planning and realisation directives.

Step 3: Planning

The planning step is normally concerned with the production of a detailed annual activity

plans. To accomplish this, a long term planning exercise (typically 5-7 years) is needed.

This plan is based upon forecasts (that take both load and non-load related issues into

account) with regard to maintaining system functionality. When problem areas are

identified, the asset management function must define one or more solutions (design,

building and/or maintenance) for each issue while maintaining a system-wide perspective.

The chosen resolutions form the basis of an annually-reviewed long-term plan (containing

estimates on start/end dates and costs). The forthcoming year of this plan is annually

extracted and forms the initial workload for the service provider(s) to the asset

management function.

Step 4: Realisation

The plan is executed in an ongoing fashion by the service provider on behalf of the asset

manager, who is responsible for monitoring and auditing the process. The results provide

the asset manager with essential knowledge for the future and are input to the long term

planning process.


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Decision Process

The asset management process as described in the previous section necessitates decision

support tools to choose the best option when presented with a number of alternative

options in a decision-making environment. This can effectively be viewed as a

continuously running decision process based upon technical, economic and social

information. Upon further analysis, this decision process can be seen as being comprised

of three separate levels, as illustrated in Fig. 5.

Figure 5: Asset management decision process

The asset information level consists of technical asset data/information and condition

assessment of components, bays, substations and networks. The financial information

level combines the economic and technical and risk data/information for these assets and

is mainly reliability-focused. The social information group applies the economic

data/information relating to the business; combined with social information, to make

decisions about risk which mainly have a corporate focus.

As a general approach to data/information processing, the decision-making process

proposed is directed at risk assessment:


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Risk/condition assessment

Based on the inputs the S3 (“Safety”, “Social” and “System – oriented”) risks are evaluated.

Safety risks represent impact on personal and environmental safety.

Social risks represent impact on external bodies directly dealing with the company,

on the public and the environment, but also internal personal-related impacts

(change of work procedures, personnel reduction etc.).

System risks represent impact on technical parameters of the assets (system or

equipment reliability, operating conditions, risks of fatal failures etc.).

A framework for this S3 risk assessment is provided by external inputs, stating constraints

and basic conditions of system operation. The data analysed during the risk assessment

also includes financial data/information and asset data/information. Different scenarios

are obtained from the S3 risk assessment. By combination of these scenarios with outputs

from condition assessment and financial inputs the reliability of an asset can be controlled

with respect to adequate costs and sufficient levels of asset reliability. System risks related

to the particular level of asset reliability are also evaluated.

In this step technically/economically optimal management of the asset is obtained as a

result of reliability management. To make the process complete, the last two “S” (i.e. safety

and social) risks must be taken into account. On this level (“Risk management”), the overall

risk related to the final decision is evaluated and controlled. It concerns not only the

technical and financial aspects, but respects also the personal and environmental safety,

impact on external bodies, stakeholders’ demands and other “social risks” aspects as

described above.

Decision flow

The decision process as illustrated in Fig. 5 can be further explained by presenting it as a

decision flow diagram (Fig. 6). This details an overall view of the step by step decision

process. A detailed description of the different levels can be found in chapter 5.1.1.


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Level 1: Component (asset) level

The main task of the first level is the assessment of the condition of the equipment.

Condition assessment is provided based on technical asset data/information. As an output

of condition assessment the probabilities of assets’ expected behaviour modes are

obtained (probability of failure, expected life, system constrains etc.).

Figure 6: Asset management decision flow

Level 2: Network level

Combination of the scenarios’ technical data/information with relevant economic data

from underlying economic systems and the information on the grid will result in a

quantification of costs and benefits for each scenario. System risks related to the particular

level of asset reliability are evaluated.

Level 3: Corporate level

The costs and benefits of the scenarios are combined with the risks involved with each

scenario to reach the optimum decision regarding the external requirements (social


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information) with the best-managed risk. The social and economic data/information,

together with the reliability of the equipment inventory and network topology forms the

ingredients of the risks involved.

Information Requirements

Having analysed the asset management accountabilities and decision processes, the next

step is to extract the information required to support asset management decisions (Fig. 7).

Figure 7: Asset management information requirements

Asset management decisions applied in the processes described earlier employ different

levels of data processing, often provided by a plethora of existing IT systems. During the

last decade many commercially available IT tools have been developed. The available tools

may be grouped into three categories:

Enterprise Asset Management (EAM): Such tools are directed at asset data storage and

management, work flow management, monitoring & diagnostic systems, geographical

information systems.


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Enterprise Resource Programmes (ERP): These are directed at the support of business

process management, financial and spares control.

Asset Operation Systems: Directed at substation and network automation, customer

information systems, energy management systems.

High end and transactional information processing tools such as data warehouses and

expert systems are also available. These can provide support in the area of gathering and

processing data in order to provide decision support.

Data Categories

The changing market environment demands effective whole-of-life management of the

assets in order to optimise economic performance while ensuring that technical

performance and any risks associated with the assets are kept within acceptable limits.

Ultimately, asset managers will be judged on their ability to satisfy the needs of

stakeholders by minimising the necessary capital and operational expenses while

simultaneously managing environmental safety and quality (sustainability). This approach

necessitates having access to data and information relating to technical, economic and

social categories (Fig. 8).


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Figure 8: Data categories approach within asset management: event risk consequences

(example)

Technical data category

Data and information for decision support within asset management is dependent on

several asset-related parameters. Technical aspects cover condition assessment, aging

models, failure probability and its impact on the system, etc. However, data and

information relating to equipment inventory, new technology, network topology and

development, generation and demand levels, available spare parts, current maintenance

procedures, history of maintenance actions, history of failures, etc. are also covered by

this category.

Economic data category

Each technical data component should have associated financial data. Costs of condition

assessment procedures (and tools) and life cycle costs (investment, operation,

maintenance and disposal) for equipment and spare parts are also contained within this

class of data/information. The costs related to a failure are dependent on the outage-


Page 28

related expenditure. Failures may result in major damage to network components and

their environment, which will lead to high maintenance and repair costs. The need for

economic data/information finds its origin in the requirements of operating a business.

For example, penalty costs from customer contracts, possible claims from customers,

costs associated with undelivered energy, damage to reputation/share price, etc.

Social data category

Risks are not only determined by economics. There are also social and environmental

aspects that must be considered, such as the impact on society of outages and failures.

Failure acceptability can be reflected as the degree to which a failure is acceptable from a

social perspective. The impact of failure depends on the criticality and number of

connections affected by a failure and is also dependent on the time taken to restore

supplies following a particular failure. The social impact of a utility’s policy is measurable

using two factors: public image and the perception of safety.

Information Pyramid

The practical implementation of IT tools to support the decision-making process is a

challenging problem as the final architecture of information systems can strongly influence

the processes and organisation of a utility.

A generic information processing architecture to support decision processes can be

identified. This is referred to as the “Information Pyramid” and is presented in Fig. 9. This

architecture allows open use of data together with high stability and a high degree of

integration. It reflects the descriptions of the decision-making processes (Chapter 2.3),

data categories and information requirements as described in this paper. Different levels

of the pyramid correspond to appropriate parts of the decision process.


Page 29

Figure 9: Information pyramid

“Front” information systems – data input layer

The base of the pyramid is populated by various “front” information systems covering the

asset management accountabilities as presented in Fig. 3. Generally, this layer is a

compound of several different information systems. The interoperability and operational

quality of such systems strongly depend on the interfaces between particular systems. The

layer provides input data and information for the decision processes as described

previously. Data and information entering the asset related decision-making process can

be looked upon as being grouped into the three basic categories – technical, economic and

social.

Data warehouse – data integration layer

The next layer concerns data storage based upon tools for basic data integration. This

integration level ensures that the data in the system are unique; it allows the basic

verification of the data and also integrates multiple items of data and information to

facilitate use by functions requiring data from a diverse range of sources. Such structured

data-integration systems are commonly referred to as “data warehouse” systems.

decisionlevel

dataanalysislevel

dataintegra onlevel

frontinforma on

systemlevel

decision

decision input

analysis

analysis tools

data models

data warehouse

ERP EAM assets EOS Masterplan

security consistency

continuity


Page 30

Characteristics of data warehouses include:

1. Enforcement of a consistent view of corporate information across all systems

concerned which facilitates explicit decision making.

2. Provision of historical data (this is useful as many transactional information systems

do not possess this functionality).

3. Presentation of a single transparent source for delivery of any available data with the

user remaining unaware of the complexity involved in achieving this function.

4. Facilitation of data quality assurance and control as shown in the quality loop of Fig.

9.

Referring to the decision process (Fig. 5), this layer allows organising, processing and

integration of data belonging to the three data categories. The data warehouse supports

the decision process level by ensuring quality, consistency and validity of data and

information.

Analysis tools – data analysis layer

The decision process continues by using techniques such as condition assessment, S3 risk

assessment etc. (Chapter 2.3). For such analyses, aggregated values and global technical,

economic and social indicators are used. This use of data and information is the key tool

for the asset management process, as it delivers a basis upon which decisions are made.

Decision support – decision layer

While commonly used analytical tools deliver general information about various business

areas based upon aggregation of measured values, other highly sophisticated decision

support tools are available and have been applied in a number of cases. Such tools may

combine the results of different analyses and data to deliver a result in the form of

decision recommendations. Therefore a specific category is assigned to this type of tool.

They are not yet commonly applied within power utility businesses.

Decision support should facilitate the final decisions to be made by the asset manager

through the provision of a list of ranked recommendations with explanatory information.


Page 31

The responsibility for the decision should always lie with a suitably qualified person – the

user of the information system. It is therefore essential for the information delivered by

the system to be fully reliable, transparent and well understandable.

Conclusions

Sustainable asset management processes are directed at maintaining the functionality of

the system while respecting the (sometimes conflicting) expectations of the various

stakeholders. The information strategy should focus on how utilities hold and manage

data and information on their transmission and distribution plant and facilities. Based

upon the approach described in the paper, information technology should focus on the

provision of support for asset management decision-making processes. The task force

recommends the application of a layered methodology in order to organize the decision

process and application of specific IT tools to the different decision and control levels. This

focussing is necessary in order to minimise the costs involved in the design or purchase of

information systems.


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IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT

UTILITY ASSET MANAGEMENT

As asset management is a synonym for risk management a model “from risk to data

management” Fig. 1 is used to format this brochure. It recognizes the essential differences

between:

a risk management approach in a business relevant environment; and

data management support for the decision making process.

The model expresses the need to align the translation of data into information according

to the risk based decision process and this process that takes into account all relevant

areas including social, technical and economic [2]. This chapter follows this “hourglass”

model; a business setting point of view will be described as well as further design of the

decision processes and data/information. The values of the different stakeholder groups

are used for the process of risk management. The process described will be in line with

stakeholders needs with regard to social, economic and technical values. In the data

management part a chapter “intelligent hub” will describe methods and approaches to

transform information requirement into data requirements. On one hand, this

transformation process can be regarded as one of the most important features for asset

managers. On the other hand, it can assist data managers to construct the asset

databases. Related to the data requirements are data processing methods (data

warehousing) and these are described that realise data extraction from existing data

bases. A description of the specific data sources for this data processing finalizes the data

management chapters. It is emphasized that the described process has an iterative

character with a “demand” or lead line coming from the business relevance instead of,

nowadays still often feasible, the technical relevance.


Page 33


management

Stakeholder orientation

The context of an asset manager is quite complex. He has to satisfy requirements from

many different stakeholders. Fig. 10 shows the asset management context and the

different types of relevant stakeholders.

To satisfy all stakeholder requirements is of the highest priority and stakeholder

satisfaction is the key success factor for an optimal supply of electricity, which can be

regarded as a public service and a basic need for all customer groups.

The electricity market players – generation, trading and sales, consumer, public and

environmental bodies as well as system operator, asset owner and service provider –

should agree upon rules, quantification of service levels and payment and penalties.

Therefore the identification and quantification of the key indicators is necessary, and this

can be done by analysing the impact on the stakeholders. Most of the impact can be

expressed in monetary terms, for example: Direct costs or profits by means of additional

costs, losses, penalties, profits and indirect costs by means of costs of secondary defects,

losses through delays etc. Social and ecological impact as environmental loss, loss of

image, safety etc. can be difficult to quantify and to express in financial terms. They can

be measured by stakeholder satisfaction, e.g. a score card system. Transparency, an open

minded and honest attitude as well as respect and confidence are important factors to

increase satisfaction.


Page 34

Figure 10: Asset management context

Risk balancing using a scenario approach

Disturbances and planned outages have an impact on both the upstream and downstream

business processes of generation, transport, distribution and consumption. There may

also be an impact on individuals and in some cases on the environment. A sustainable

asset management process has therefore to consider technical, economic as well as social

and environmental aspects and must find a balance between all stakeholder values and

requirements, Fig.11. The responsibility is on a much higher level than just the substation

level or the system operation level. The characteristics of high-end asset management and

a good service level is the satisfaction of all stakeholders. However, the different

stakeholders often have different points of view and different expectations.

Nowadays, senior management faces a lack of well-prepared information, skills and

decision supporting tools. In some instances this may lead to polarized strategies, which

fail to satisfy the previously mentioned objectives of sustainability and overall stakeholder

satisfaction. Corrective measures, e.g. from regulatory or other bodies, become necessary.


Page 35

Risk management is seen as one of the most important instruments for asset

management. Considering the above mentioned aspects possible risks for all stakeholders

have to be identified and the impact has to be analysed.

Figure 11: Sustainable asset management process in which risk balancing of technical,

societal, economic and environmental aspects is of importance

Multiple scenarios can be found to demonstrate the influence performance at all three

levels: e.g. increasing or decreasing maintenance and inspection intervals, replacement or

refurbishment, changing the maintenance strategy. But also the combination of technical

information with relevant economic data and future system performance will result in

quantifiable benefits. Not exclusively to be expressed in economic terms but also in terms

of reliability. On the corporate level, balancing the cost and benefits of the scenarios with

the risk involved with each scenario will result in the strategic decision with the best-

managed risk outcome. On this level, all stakeholders’ expectations can be taken into

account when they are formulated as business values, resulting in a set of performance

indicators to give expression to those expectations. The societal and economic

Asset Management

Process

Technical

Societal

Environmental

Economic


Page 36

information, together with the reliability of the equipment inventory and the system

performance form the ingredients of the risks involved. A balancing of the risks will lead to

the final decision.

The intelligent hub

From an information-oriented perspective, the risk management process can be thought

of as propagating in two directions.

The first direction is from the business risk/asset management strategy (i.e. high-level

decision-making processes) towards the data that is required to provide information for

decision making. In this “top-down“ path the overall business objectives are leading to a

set of information requirements that can support decisions and can be considered as the

foundations of the risk management process. From these information requirements the

basic data requirements for the source systems can be derived. Those requirements also

include “how” and “when” data should be presented to the users and other processes. All

of these requirements influence data organization and architecture.

The second direction comes from the data sources towards the business risk/asset

management strategy. In this “bottom-up” path data is extracted from the operational

systems, transformed and stored in a data warehouse and finally presented as valuable

information, taking into account “how” and when” the data should be used to support the

decision making in the risk management environment. The top-down path is the typical

view when ‘designing’ the system and specifying the requirements. Sometimes there is a

misunderstanding that the information systems can deliver strategic recommendations by

themselves, which may lead to an underestimation of the necessary effort required in the

“top-down” part of the process. In fact, it is the “top-down” path of specification of

information requirements and key indicators resulting from the strategic goals, which form

the core know-how of the whole risk management process. The “bottom-up” path is then

used in day-to-day operations for translating data into information on the predefined

basis. This two way transition needs intelligent support provided by humans, software or

both. For further clarification this point is identified as the “intelligent hub” with the

structure and mechanisms proposed further in this chapter.


Page 37

Asset oriented model

An asset oriented data model forms the foundation of a system to support asset/risk

management. To position a component in the IT structure while maintaining a link to its

actual position within the system, such a model should take account of:

the hierarchical structure of equipment:

o enterprise;

o network/system;

o substation;

o bay;

o equipment/apparatus; or

o further break down of the equipment to sub-parts (e.g. parts of a circuit-

breaker).

the exchange of asset data between different equipment and different stakeholders

within the enterprise having various objectives and data requirements.

The detail of hierarchical structure breakdown needs to be carefully defined with regard to

the required level of information detail. Too much detail information can sometimes

decrease effectiveness of its use. Essential to an asset oriented model is the separation

between functional objects and physical objects (equipment). This concept allows

connecting ERP, Geographic and SCADA type of information systems. An example of such

an object approach and tree structure is shown in Fig. 12.


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Figure 12: Example: Object approach – tree structure

As stated previously standards in definition are very important. As such a standard, which

allows a differentiated approach, a dual asset description is proposed. In this model the

asset is described from two points of view:

Functional object approach involves the description of assets as non-material objects, i.e.

its function and relative position in the system. Thus, an object can be seen as a part of

the scheme with possibility to build a hierarchical structure of objects (see Fig. 12). Each

object can have a “parent object” and a “successor object” (e.g. network of given voltage,

substation, bay, generic position of a given instrument etc.). A functional object is system

inherent and doesn’t change unless a reconstruction of a system or its part takes place.


Page 39

Specific component approach is a description of asset as a real piece of hardware,

occupying a generic position in a network, providing the required function and having

specific, well-defined properties. This approach does not permit a hierarchical description

of the network; however its simplicity is beneficial. Phenomena, such as change of

properties of component, which are independent on the component’s position, can be

studied separately from the network phenomena.

Both approaches are complementary and allow separate solution of complex problems. It

is not only a method of representing and processing asset data, but a philosophical

approach to the support of the asset management function. It also identifies the unique

assets for the key register as a part of the data warehouse.

Dimensional model

Another way of structuring data is with regard to its value for the asset manager. A

technique of dimensional modelling can be used to organize data in a manageable way. A

simplified example is added in the Fig. 13.

Figure 13: Dimensional model

This technique describes dimensions required and measures on a certain subject, each

with its own level of detail. Using this technique, results in a single A4 format paper can


Page 40

represent many different kinds of reports. Explanation of the diagram: two possible

reports or information needs are described.

In red: A manager is interested in quarterly costs per substation per main process

(e.g. maintenance) per department.

In blue: A business analyst is interested in a monthly report on hours on handling

outages (= as a selection of one of the sub processes) for the whole enterprise.

Many other information needs could be described this way. Adding one dimension (e.g.

“location”) or one measure (e.g. “number of outages”) reveals a whole new set of

information needs. This dimensional diagram is one of the principles of “data

warehousing”. Based on that concept, data is collected in the data warehouse from the

source systems and presented in a way set by the model set-up so that an asset manager

or asset analyst can easily navigate through the data.

Processing data

The asset manager is interested in the coherence between Technical, Economic and Social

data on three levels: “Corporate”, “Network” and “Asset”. The integration of information

therefore is essential in this point. The second issue of high importance is data quality in a

means of complete, correct and actual data. Experience shows, that assuring the data

quality can consume 10-50% of the total (information) project budget and is also the cause

of trespassing many deadlines.

The architectural overview

The diagram in Fig. 14 shows as an example an architectural overview from both technical

and organisational perspectives. Concerning data objects, we can consider any object or

entity of a technical relevance, such as circuit breaker, transformer or connection of

corporate relevance, such as a customer or contract etc. In the following figures a circuit

breaker is used as a practical example.


Page 41

Figure 14: Data management (technical and organizational)

In the figure above, the circle represents examples of present applications (ERP’s,

Geographical Information System, Maintenance system etc.) that support the processes of

the asset manager. Organisations or departments are free to choose their own applications

as long as they refer to the key register. All systems then hold different information

(technical, financial, environmental, etc.) about the same data object (e.g. a circuit

breaker). The key part of the system placed in the centre is a key register. This register

synchronizes key data concerning the main data objects as defined in the former chapters.

The essential fact is that this register only contains keys, pointers to keys in connected

systems and a very limited number of data elements that identify the object in the real

world, so that data duplicity is avoided. As an example for a circuit breaker this can be

serial-number and manufacturer.

For analysis and reporting purposes relevant data of the circuit breaker [i.e. location (from

GIS), last date (from the maintenance system), costs (from ERP) etc. are periodically

extracted from the source systems and loaded into the data warehouse. This way data

history can be built for trend analysis.


Page 42

A presentation layer provides the end user with data in a readily usable way. Based on both

a pull and a push strategy one can analyse and report on data both from the warehouse

and from the key register. This way of presenting data is often referred to as BI or Business

Intelligence.

Since data has the tendency to degrade with time, data needs to have the constant

attention of the organization. Data monitoring and improvement related to the real world

is therefore needed. Audits can contribute to maintaining data quality. The organization

should also use the advantages of available techniques to maintain synchronization of the

systems and to support new systems with required data obtained via the data warehouse

from the source systems.

Benefits

This architecture offers the business the following benefits:

It offers the business one holistic view. This reduces search-time and work (typing)

for the user;

Better data quality by easily comparing data in different systems;

The key register offers a better “time to market” for data warehouses;

Systems can be disconnected without the loss of data;

Reduced “time to market” for new systems because they can be loaded with good

quality data;

New service providers can be easily attached even if they use different systems. This

principle also counts for decentralized business units; and

Service providers or decentralized business units can choose their one

functionality/systems while central departments or the asset manager, as a spider in

the web, can keep the necessary data.

These benefits can be reached with minimal costs and without interrupting the operational

process as the present applications stay in operation.

Conclusions and further outlook


Page 43

A practical approach to implementing an asset management information strategy was

outlined using the “hourglass” model to illustrate the transition from risk management into

data management. The process of risk management needs to ensure satisfaction of

requirements of all stakeholder groups and relevant constraints with the final aim of

social, economic and technical issues fulfilment. Nowadays different scenarios are often

used to support a decision process. Each scenario should take social, economic and

technical consequences into consideration. For the scenario evaluation process, data

obtained from both system operations as other corporate processes need structured

design. This structure is referred to as the “intelligent hub”, a principal description of

transition from data into valuable information necessary for further processing. This “hub”

is an important part of the utility’s information processing and may be a keystone for a

successful information strategy and risk management philosophy. Data warehousing,

finally, is necessary to link different key data elements from different information systems

in order to support the data management process. This support must be given in a multi-

dimensional and consistent approach.


Page 44

IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT

UTILITY ASSET MANAGEMENT - DECISION MAKING WITH REGARD

TO STAKEHOLDER ORIENTED RISK MANAGEMENT

The first information strategy publication [2] as per chapter 3 concluded that Information

Strategy focuses on risk management, being the most important responsibility for the

asset management function. Risk management identifies all credible risks; ascertaining

and quantifying their potential impact and incorporating this information into all asset

management decision processes. Risk management takes into account all relevant

consequences from economic, technical and social perspectives. This risk-based decision

process is complicated by the fact that both short-term, day-to-day decisions and longer-

term strategic decisions must be supported. Furthermore, there are several, sometimes

conflicting constraints and conflicting stakeholders’ requirements that must be taken into

account. The different activities should be executed in an order that depends on the

contribution to the quality of energy supply. It must, however, also satisfy the

requirements regarding economic added value and safety aspects. The asset manager is

thus facing a continuous process of decision-making based upon 5

forecasting of needs and system development;

consequences on system performance;

influence on (regulated) price, costs and profit; as well as

impact to safety and society.

Risk assessment also necessitates the comparison of different scenarios of system

development, maintenance and (re)investment strategies, major and minor failures (e.g.

probabilistic or deterministic) etc. 6, 7, 8. Risk assessment and related information

management form a very important part of the end-to-end process of asset management.

In many utilities, data is collected and stored in line with financial responsibilities and give

some basic information about the assets concerned. However, a common and structured

approach to the assessment of the risk associated with these assets is not applied.


Page 45


management

The decision process considering various scenarios

As a structured approach that assesses data into asset decision information follows from

the description of the three different decision levels as given in the publication [2] which is

summarized in the following Fig. 15.

Figure 15: Information flow and processing for decisions

Risk Assessment

Demanded reliability,

availability and quality;

Undelivered energy price, Claims, Fines

Scenarioswith quantifiedbenefits or costs

3: Risk Management“Decision”

2: Economical evaluationof different alternatives,Creation of maintenancestrategies, life curvesand life cost computation

Investment costs

Outage costs

Spare part costs

Diagnostics costs

Maintenance costs

Replace

Run to failure

Refurbish

CBM/TBM/CM

M/D modification

As Before

Monitoring

Scenarios

Selection of relevanteconomic data

Repair costs

Equipment Inventory

Failure cases

Spare parts

Diagnostics

Maintenance

Aging Models

Reliability evaluation

Maintenance rules

1: Maintenance and diagnostic evaluation, scenario search, evaluation of their usability

asset level

network

level

corporate

level

Customer contracts

Safety regulations

Regulator

Business Values

Component level

System level

Corporate level

1:

Maintenance and diagnostic evaluation,

scenario search and evaluation of

effectiveness & usability

2:

Economical evaluation of scenarios,

creation of maintenance strategies,

calculation of non-delivered of energy

3:

Risk management,

selection of optimal scenario,

and optimal strategy

Technical

information

(on assets)

Economical information

(on assets)

Technical information

(on grid)

Economical information

(on business)

Sociological information

Decision

System performance

Power qaulity

System development


Page 46

Level 1: Asset / component level

From a technical point of view, multiple scenarios can be used to illustrate influence on the

performance of assets. For example, scenarios could be increasing or decreasing

maintenance and inspection intervals, replacement or refurbishment, but also changing

the maintenance strategy from corrective maintenance to time based or condition based

maintenance.

The scenarios are found by analysis and combination of the equipment inventory, the

maintenance actions already performed on the equipment, the current rules that exist for

maintenance, and condition assessment, that results from diagnostics, in combination with

statistical evaluation of practical failure cases, reliability evaluation and ageing models

result. All these scenarios will have a different effect on the technical performance (in

terms of reliability and availability) of the asset. At this stage, scenarios should not be

excluded based on their effect on technical performance, because in the next levels, the

scenarios will be combined with other relevant data, which even may cause a scenario with

a negative effect on technical performance to be the most economic while having the

smallest risk. The stakeholders’ expectations for example will be taken into account at the

third level.

The decision to replace equipment by different scenarios may not only depend on the

increased failure rate but also for example on the acceptance of the used technique, the

know-how of the service department, availability of spare parts, or the current capability

of the considered equipment.

Level 2: System level

Technical information of the scenarios with relevant economic data is not exclusively

expressed in economic terms, but could also be expressed in other terms such as

reliability or expected lifetime.

The main aspects of the system performance such as network topology and development,

changing of the generation and demand and so on may influence the decision process

regarding the asset. Possible consequences and impacts on the system with different


Page 47

scenarios are obtained from reliability calculations (system reliability, operating conditions,

risk of fatal failures etc.). The reliability calculation of the non-delivered or supplied

energy at certain system nodes is an important result to assess the performance of the

system from the customer point of view (Delivery Point Performance or DPP). This will lead

to a reliability centred approach to maintenance for the complete system.

In this step of the decision-making process, modern methods of maintenance evaluation

and planning can be used (e.g. Reliability Centred Maintenance, Risk Based Maintenance

etc.).

For example, enlarging an inspection interval may cause a benefit in decreased

expenditures, but could have costs in terms of decreased reliability. On the other hand,

shortening the interval might have costs in increased expenditures, but could lead to

benefits in increased reliability.

Level 3: Corporate level

On the corporate level, balancing the costs and benefits of the scenarios with the risk

involved with each scenario will result in the strategic decision with the best-managed

risk. On this level, all stakeholders’ expectations can be taken into account when they are

formulated as business values, resulting in a set of performance indicators to give

expression to those expectations. To make the process complete, the safety and social

risks must also be taken into account. On this level (“Risk management”), the overall risk

related to the final decision is evaluated and controlled. It concerns not only the technical

and financial aspects, but respects also the personal and environmental safety, impact on

external bodies, stakeholders’ demands and other “social risks” aspects as described

above. Safety risks represent impact on personal and environmental safety. Social risks

represent impact on external bodies directly dealing with the company, on the public and

the environment, but also internal personnel-related impacts (change of work procedures,

personnel reduction etc.).

The societal and economic information, together with the reliability of the equipment

inventory and the system performance form the ingredients of the risks involved. A

balancing of the risks will lead to the final decision.


Page 48

The current used performance indicators were collected and summarised by the WG CIGRÉ

1.11 and published in Brochure 367 “Asset Management Performance Benchmarking”

(2008) [9]. In this chapter the different Key Performance Indicators (KPI) are related to five

classes: finance/business, safety, reliability, customer and employee, which can be

allocated to the overall groups: technical (reliability); economic (finance/business) and

social (safety, customer, and employee) as mentioned in chapter 3.2.3 and 3.4.1 of this

brochure.

Example scenario approach

For every decision to be taken there are a certain number of possible scenarios [7]:

status quo by means continuing as before (1)

replacement of all equipment (2)

refurbishing the most critical equipment (3)

re-design the substation concept (4)

Some generic questions need to be answered to define which scenarios have to be

considered [10]:

Are there planned or intended changes in the electric power system?

Are there planned or intended changes in the organizational structure?

What is the technical relevance and importance of the substation or bay etc.

concerned?

What is the business relevance and importance of the substation, bay etc.

concerned?

To assess the risk each of the scenarios has to be checked on the mentioned key aspects

and the key performance indicators (KPI’s). Which of the indicators is used depends on the

actual project and the corporate level’s interest to achieve the target of the company, for

example in the area of social aspects. The information which should be used defining the

key performance indicators are listed in 5.2.3 and Table 1 shows an example.


Page 49

Scenario Key

aspects

KPI’s

Scenario 1

(e.g. extending life time)

technical KPI 1 (e.g. remaining life duration)

KPI 2 (e.g. failure probability)

…

economic KPI 3 (e.g. revision costs)

KPI 4 (e.g. life cycle costs)

…

social KPI 5 (e.g. environmental impact)

…

Scenario 2

(e.g. renewal by replacement

of all equipment 1:1)

technical KPI 6 (e.g. asset condition)

KPI 7 (e.g. reliability)

...

economic KPI 8 (e.g. re-investment costs)

KPI 9 (e.g. business opportunity)

…

social KPI 10 (e.g. safety)

KPI 11 (e.g. image)

…

Scenario 3

(e.g. renewal by refurbishment

of the most critical

equipment)

technical KPI 6 (e.g. asset condition)

KPI 7 (e.g. reliability)

...


KPI 12 (e.g. following re-

investment)

…

social KPI 2 (e.g. failure probability)

…


Page 50

Scenario Key

aspects

KPI’s

Scenario 4

(e.g. redesign the

configuration of a substation,

i.e. reduce the number of

busbars or eliminate the

transfer bus)

technical KPI 13 (e.g. power supply quality)

KPI 14 (e.g. availability)

…


KPI 4 (e.g. life cycle costs)

KPI 9 (e.g. business opportunity)

…

social KPI 15 (e.g. employee motivation)

KPI 11 (e.g. image)

Table 1: Example of different scenarios, the key aspects and relevant KPI’s

Requirements

Stakeholder Requirements

The electricity market players – generation, trading and sales, consumer, public and

environmental bodies as well as system operator, asset owner and service provider –

should agree upon rules, quantification of service levels and payment and penalties.

Therefore the identification and quantification of the key indicators is necessary, which can

be done by analysing the impact on the stakeholders. Most of the impact can be expressed

in monetary terms:

Direct costs or profits by means of additional costs, losses, penalties, profits and

Indirect costs by means of costs of secondary defects, losses through delays etc.

Social and ecological impact as environmental loss, loss of image, safety etc. is difficult to

quantify and to express in financial terms. They can be assessed by stakeholder

satisfaction, e.g. using a score card system. Transparency, an open minded and honest

attitude as well as respect and confidence are important factors to increase satisfaction.

The following Table 2 shows the most important requirements of the particular

stakeholders. Economic aspects are of most importance for all stakeholders which are


Page 51

running a business in a competitive, privatized environment. Public owned companies,

probably with a monopoly, and public service providers (e.g. medical service, water &

energy supply, transport, broadcasting and communications as newspaper, radio, TV,

internet and telephone etc.) are more focused on technical requirements as quality and

reliability. The environment (air, vegetation, animal life etc.), represented by environmental

protection organisations, and the human being are practically purely social focused

(existence, safety, convenience and comfort, motivation, fun etc.). Political organizations,

legislator and regulator are balancing interests between market forces, public basic needs

and power consumer needs.

Stakeholder Economic Technical Social

Trader, sales - Max. profit

Private generator, IPP - Max. profit - Availability

Public generator - Profit - Reliability

- Low risk

- Green power

- Safety

Asset owner - Max. profit - Availability

- Asset condition

Supply industry - Max. profit - Product,

quality/price

Service provider - Max. profit - Service level/price

System operator - Profit - Reliability

- Power quality

- Solidarity

- Cooperation

- Safety

Employee - Salary - Existence

- Motivation

Political

organisations

- Market

structure

- Fair prices

- Market justice

- Political

balanced

Regulator, legislator - Controlled

prices

- As much as

necessary

- Benchmark

- Legal

conformity

Private industry - Max. profit - Availability


Page 52

Stakeholder Economic Technical Social

Public service

provider

- Profit - Reliability

- Power quality

- Safety

- Image

Private consumer - Low prices - Availability - Safety

- Comfort

Public - Low prices - Availability - Safety

Environmental - Existence

- Safety

Table 2: Stakeholders and their requirements according to their values

The table shows the tendency from economic aspects of purely business driven

organisations towards the societal aspects of more public, human and environmental

oriented organisations. In any case, stakeholder satisfaction is directly proportional to the

fulfilment of his particular requirements and inversely proportional to the risk of not

fulfilling his particular requirements.

Information requirements

The aspects of information needed to support asset management decisions can be divided

in three main categories according to the described aspects of Fig. 11:

Technical aspects;

Economic aspects; and

Social aspects.

These different aspects are described in more detail below.

TECHNICAL ASPECTS

The technical aspects consist of two different parts: information about the single

equipment item as well as information about the system development.

Relevant information needed for asset management decision support is dependent on

several asset-related parameters, such as the insulation ageing characteristics of a

component and the probability of an over-voltage across a component or to earth, e.g. as

a result of switching activities. The component ageing is related to the operational age, the


Page 53

type, the history and the operating conditions of a network (sub) component. In order to

decrease the probability to a failure, condition assessment can be used. A combination of

diagnostic tools for condition assessment is chosen and applied, depending on the

different types and locations of insulation defects induced degradation sites.

The technical aspects cover amongst others, condition assessment, aging models, and

failure probability, but also information on the equipment inventory, the system

performance (quality of energy supply), the available spare parts, the current maintenance

procedures, the history of maintenance actions, the history of failures and the

manufacturer. Consideration of the equipment condition or the probability of outages is

the basis to evaluate the system performance by utilization of reliability calculations. In

principle there are two levels to perform reliability calculations:

Equipment approach: This can be called as outage-oriented criteria. That means the

probability of an equipment outage is evaluated on the basis for example on several

condition information.

System approach: Complementary to this there are the customer-oriented criteria.

In this case the frequency and duration of the interruption of supply at certain

system nodes is calculated (non-delivered energy), which is caused due to the

outage of equipment in the network. The result is that the non-delivered energy has

to be considered to determine if this situation is acceptable for the customer or not.

In the same way the non-supplied energy needs to be considered for example, that

a power plant is not able to feed the system.

In principle customer-oriented criteria are only useful to fulfil the requirements of the

customer as well as the utilities and therefore to apply certain maintenance measures.

ECONOMIC ASPECTS

As every technical aspect will have its financial counterpart, economic aspects include the

costs of maintenance, repairs and failures, but also the costs of condition assessment and

the investment costs for equipment and spare parts (life-cycle costs). These costs will be

called the economic information on assets.


Page 54

The costs related to a failure are dependent on the outage-related expenditure. Failures

mostly result in major damage to network components and their environment, which will

lead to high maintenance and repair costs. In case of major critical damages, professional

solutions are needed to restore the energy supply as quickly as possible, eventuating in

higher expenditures. In addition minor failures which are repaired during planned outages

have also to be taken into consideration, because they will also influence the costs.

Outages can result in customer compensation and responsibility claims in the form of

penalties. The need for economic information finds its origin in the fact of driving a

business, e.g. penalty costs from customer contracts, possible claims from customers and

the costs of undelivered energy. These aspects will be called the economic information of

the business.

SOCIAL ASPECTS

However, within the asset management decision process, technical and economic aspects

are not the only aspects to consider. As an example, risks are not only determined by the

economics. There are also some societal aspects that have to be considered, such as the

impact on society of outage and failures.

Failure acceptability can be reflected as the degree in which a failure is acceptable from the

social point of view. The failure impact is dependent on the criticality and number of

connections, which is further affected by the time to restore the particular failure. Even so,

frequent energy interruptions in a short period of time will not be acceptable from a social

point of view. For example, power losses related to buildings with a high social standard,

e.g. hospitals have a low acceptability level.

Furthermore, the social impact of utility’s policy is determined among others by two

factors: the image to the public and the feeling of safety. Power supply disturbances in

buildings with high public exposure e.g. shopping malls, congress centres; hotel office

towers, airports etc. have an impact on public opinion of safety.

Some of the societal aspects will be guarded by the regulator, which will translate these

aspects in proper regulation. This regulation will result in economic impacts such as

penalty costs. Other societal aspects such as the image of the power utility or the personal


Page 55

safety of its employees cannot be easy translated into economic aspects, but should also

be covered when investigating risk.

This societal impact can have very strategic consequences for an organisation. Customers

can show the tendency to change energy supplier so a successfully operating asset

manager (from the perspective of lower maintenance costs) can cause a utility large

damage (due to high failure rate).

Key performance indicators (KPI’s)

The information and key parameters needed by the utility for supporting risk management

decisions and actions are related to the risk model presented in chapter 4.1.2. These key

parameters are based on some generic data, derived from the overall usable and available

data pool in the enterprise in a data processing and condensing process.

The key parameters for risk management purposes are typically the results of a previous

processing and combination of available information in a data condensing and information

extraction process based on human experience, artificial intelligence and software tools.

Also they may contain other decision relevant information, on the system, on the

environment, or social/legal context and which should help towards decision making. This

data or information is obtained from the real or virtual data warehouse of the enterprise.

As an example, the following information regarding the aforementioned aspects should be

taken into consideration when defining a key performance indicator, according to the

company’s strategy 3, 11.

Economic information:

Life cycle costs

Costs (CAPEX, OPEX)

Liability costs due to third party damages

Prices

Direct loss of profit (business could not be made)

Indirect loss of profit due to delay of the business

Costs of non-delivered or non-supplied energy


Page 56

Penalties

Costs of secondary defects (e.g. environmental or medical defects)

External additional costs or loss of profit because third parties could not make

business

Business opportunities

etc.

Technical information:

Reliability, availability of the equipment

Failure probability, MTBF, MTTR (on system level)

(n-1) security

Outages planed, unplanned

Power supply quality (Energy not supplied, System Average Interruption

Duration/Frequency Index, SAIDI/SAIFI)

Service level

Asset condition

Remaining life duration

etc.

Social information:

Stakeholder satisfaction and customer satisfaction

Safety level, failure rate, accident rate

Damages of the property in case of an outage

Social aspects of the customer (interruption of supply)

Employee motivation

Environmental load and impact (land use, pollution, electrical and magnetic fields)

Attitude: open minded, honest and respectful and confidence

Image of the company

etc.


Page 57

Combining different KPI’s (indexing)

All types of information should be weighted and brought in relation with other

information. E. g the condition index of an asset weighted with the importance index (Fig.

16a) gives the priority of investment or service activities. This example represents the

procedure in case of high voltage circuit-breakers according 12 (Fig. 16b).

A

B

Figure 16: Example of combining different KPI’s for a group of equipment (A: Condition

and importance assessment. B: Prioritisation of the service activities).

0

20

40

60

80

100

0 20 40 60 80 100

Wichtigkeit

Zu

sta

nd

Instandhaltung

Erneuerung

co

nd

itio

n

importance

service

replacement

80

circuit-breaker

0

20

40

60

A B C D E F G H I J K L M N

Dri

ng

lich

keit

ran

kin

g

priority 1 priority 2


Page 58

Conclusion

This part has in general a stakeholder orientation. The top – down approach from all

stakeholder requirements to information and data requirements as well as methods

supporting risk assessment are discussed. Risk management has been concluded as core

business for sustainable asset management.


Page 59

SURVEY ON PRACTICAL APPLICATION OF ASSET MANAGEMENT

INFORMATION STRATEGIES

The task force distributed a survey in the year 2006/2007 to obtain more information on

the applied asset strategies and their related data management. The focus of the questions

was on:

Experiences, current practices,

How IT-tools can support?

Which methodologies are being used?

The questions are grouped as follows:

Data sources,

Data processing and data warehouse,

Data requirements,

Information requirements,

Scenario approach and risk balancing,

Decision and control.

This brochure presents the survey results from 19 utilities from Europe, North America,

Middle East and Australia. The answers in the diagrams are therefore always related to the

total number of respondents (19). To keep the survey within a manageable size, not all

fields of interest could be addressed in detail. The fields of interest covered and the results

obtained are presented in chapters 6.4 to 6.9.

Experiences

Experiences and usage of current software tools in a practical way are considered.

Related fields of interest:

Experiences with existing ERP’s and EAM’s,

Differences, strengths and weaknesses, pros and cons of the current systems used,


Page 60

What use do the suppliers claim to fulfil and how far do they actually fulfil the

utility’s requirements,

Perceived deficiencies.

Access to data and conversion into information

Different standalone systems are available nowadays which are more or less integrated in a

proprietary IT-environment. It is a strong requirement from asset managers to get easy

access to data and information of all systems, to avoid data redundancy and to have

information on a common platform for further applications. The following type of data and

information needs to be considered:

Structured technical information about assets and the system/grid (e.g. asset

condition, power quality, system availability etc.),

Structured economic (quantitative) information,

Unstructured social (qualitative) information (e.g. environmental impact, stakeholder

satisfaction etc.).


Technologies to share information (e.g. data warehouse),

Technologies to process data and convert them into usable information,

Technologies to access data (e.g. browser, interfaces with other systems,

presentation to the user).

Software tools to support decision making

As mentioned above, the utilities’ requirement is to obtain a reasonable management

information system (MIS) that allows the application of an asset and data management

methodology as well as easy software tools to simulate different scenarios.


What existing software tools can support in the different field of maintenance,

refurbishment and investment decision making?

What is necessary to allocate and to get access to the necessary structured and

unstructured information (e.g. data warehouse)?


Page 61

What management information systems (MIS) are available which are in line with the

requirements (focus on risk management and decision making)?

What is the reasonable functionality of software tools?

Data Sources

The survey covered data sources with a total of 18 questions (Table 3) regarding:

data recording of primary and secondary equipment (Q7- Q9)

outage and performance data (Q10 - Q13)

maintenance/repair and history (Q14 - Q16)

diagnosis and monitoring data (Q17, 18)

actual and planned data collection in IT systems (Q19, 20)

actual and planned front-end IT systems (Q21 - Q24)

Question

number

Question

7 How do you register installed power equipment?

8 How do you register installed automation, control and protection

equipment?

9 How do you register details on location?

10 How do you record failures for power equipment?

11 How do you record failures for control and protection equipment?

12 How do you record operational data?

13 Do you use operational history for AM decisions?

14 Do you record maintenance and repair data?

15 Can you re-construct the maintenance/repair history from your records?

16 How long back can you re-construct the maintenance/repair history?


Page 62

17 Do you record diagnosis (off-line) data?

18 Do you record monitoring (on-line) data?

19 How do you collect data and enter it in your systems now?

20 How do you plan to collect data and enter it in your systems in the

future?

21,22 What front end systems do you use now?

23,24 What front end systems do plan to add in the future?

Table 3: Questions on data sources

Recording of primary and secondary equipment

For AM, especially in the high voltage power equipment field, various data sources are still

used (Fig. Q7 and Q8), in which basic as well as detailed information is registered.

Different media such as paper (excel) files and databases are used at local, departmental

and enterprise level. The enterprise-wide equipment database though is the most

predominant in the branch (79% of the respondents).

This is less distinct in the installed automation, control and protection equipment, for

which 63% of the respondents use the enterprise-wide equipment database. Registration

in other data sources is slightly higher than with high voltage power equipment.

The highest information detail is recorded at the substation (68%) and bay levels (74%).

Some utilities (32% of the respondents) register equipment location also using geographic

coordinates – Fig. Q9.


Page 63

Still, a substantial part of recording is done outside the enterprise-wide database. This

raises questions on the data-integration, hence questions on data quality and decision

quality.

Outage, performance and failure data

In the survey only questions related to failure data have been addressed. A pretty detailed

failure recording in the high voltage power equipment and in the installed automation-,

control- and protection is used (Fig. Q10 and Q11), though in about 40% of cases failure

consequence is not recorded.

WG remark: Only 63% of respondents are reporting major failures according to CIGRÉ

definitions. It is possible that companies use internal standards to define major failures,

which are not in compliance to CIGRÉ’s definitions. Most respondents (75%) record this

Q7: How do you regis ter ins talled power equipment

0,32

0,53

0,79

0,16

0,11

0,26

0,26

0 0,2 0,4 0,6 0,8 1

bas ic information

detailed informaiton

enterpris e databas e

department databas e

local databas es

files

paper

Q8: How do you regis ter ins talled automation, control and protection

equipment

0,26

0,42

0,63

0,32

0,26

0,42

0,26

0 0,2 0,4 0,6 0,8 1

bas ic information

detailed informaiton

enterpris e databas e

department databas e

local databas es

files

paper

Q9: How do you regis ter details on location?

0,68

0,74

0,32

0 0,2 0,4 0,6 0,8 1

loc ation s ubs tation

bay

c oordinates


Page 64

kind of operational history data and use it for decision-making Q13. The kind of data used

is shown in Fig. Q12.

WG conclusion: Although only failure data have been considered in the survey, mainly due

to focus on asset issues, also system performance data, such as un-planed and planed

outages, and load optimization/balancing, are important to the asset manager. It is

therefore recommended to keep asset performance data and system performance data

separately with mutual links between related events to enable cause – consequence

analyses. Another important issue is that besides the existence of an enterprise-wide data

source, departmental data sources for equipment also exists. This could lead to

inconsistent data within the same enterprise.

Maintenance and repair data

Maintenance and repair data seems very important for power equipment as well as

automation, control and protection equipment (Fig. Q14). An average of 76 % over all the

equipment is established. Information regarding work performed can be well reconstructed

Q10: How do you record failures for power equipment

0,63

0,63

1

0,89

0,84

0,58

0,68

0,79

0,63

0 0,2 0,4 0,6 0,8 1

major failure (C IG R E )

minor failure (C IG R E )

failure des cription

caus e of failure

time of failure

failure cons equences

actions after failure

faulted item

fault recording

Q11: How do you record failures for control and protection equipment

0,58

0,53

1

0,84

0,84

0,63

0,63

0 0,2 0,4 0,6 0,8 1

major failure (C IG R E )

minor failure (C IG R E )

failure des cription

caus e of failure

time of failure

failure cons equences

actions after failure

Q12 How do you record operational data?

0 0,2 0,4 0,6 0,8 1

not recorded

peak values

load pattern

time in operation

operational events

number of operations (where it applies)


Page 65

but time and worker information is less well traceable. Also, to know something about the

contractor is more important than knowing something about a "Worker" (Fig. Q15).

Maintenance is well recorded. From the respondents, most can reconstruct

maintenance/repair history for more than 10 year back depending on type of equipment

(Fig. Q16). Respondent refer to digital and paper records.

Diagnosis and Monitoring data

Two questions dealt with recording diagnostic (offline data) and monitoring data (online

data) as shown below in the combined Fig. Q17 and Q18. The answers show that the

offline data recording for power equipment is more common than online data recording.

There is a high degree of recording diagnostic (offline) data, especially in the case for

circuit breakers (95%) and transformers (89%). Transformer online monitoring is also well

represented (58%). Monitoring for cables and circuit breakers is less well represented (16%,

respectively 21%).

Q14: Do you record maintenance and repair data?

0,84

0,79

0,74

0,79

0,79

0,74

0,63

0 0,2 0,4 0,6 0,8 1

c ircuit breaker

trans former

cables

G IS s ubs tation

overhead lines

ins t. trans formers

control/protection

Q15: C an you re-c ons truct the maintenance/repair his tory from your

rec ords ?

0,26

0,58

0,58

0,21

0,58

0 0,2 0,4 0,6 0,8 1

time only

part repaired

s ervic e work done

worker

detailed information

Q16: How long back can you re-c ons truct the maintenance/repair

his tory?

0,32

0,26

0,32

0,32

0 0,2 0,4 0,6 0,8 1

0-5 years

5-10 years

10-20 years

more then 20


Page 66

WG remark: Information on the present condition and the trend of an asset is very

important. Both options diagnostics and monitoring are valuable and should be

considered. Future developments on intelligent grids will probably provide much more

valuable information about asset condition than obtained from present grids.

Data collection and systems used

Many respondents (68%) collect data “by hand”. Some automatic data collection from an

asset is used by 42% of respondents. Most of this data is stored in a digital format,

although storage on paper is still used. This is definitely going to change according to the

respondents. As can be expected, there will be an increased automatic collection and

storage in digital format as on the other hand there will be a decrease on collection by

hand and on storage on paper (Fig. Q19 and Q20).

According to the survey results, the respondents intend to increase their data collection

actions in several ways: planned actions, time- and event triggered actions. However, the

answers also indicate that data-collection is not rapidly changing since there are no

significant differences between the “how do you collect data now” and “how do you collect

data in the future” questions.

Q17: Diagnosis (offline)… and Q18: Monitoring (online)

data for various type of equipment

0,95

0,89

0,68

0,68

0,63

0,21

0,58

0,16

0 0,2 0,4 0,6 0,8 1

for circuit breakers

for transformers

for cables

for GIS

for inst. transformers

Q17: Diagnosis (offline) Q18: Monitoring (online)


Page 67

Questions were also asked on the usage of systems (Fig. Q21-Q24). The questions focused

on which systems utilities use nowadays and which systems utilities plan to implement.

Answers show that the whole range of (systems) functions commonly needed in the field of

asset management are used or are planned for. Since some utilities indicate that they plan

to add some of the systems it can be concluded that not all the utilities own the complete

range.

Answers also indicate that systems for Enterprise Asset Management, planning/scheduling

systems (not ERP), MS office and specific systems and proprietary databases are most

commonly (> 50% of the respondents) used. On Enterprise Resource Planning (ERP) a

significant increase on adding those to the existing system landscape can be seen.

WG remark: Many utilities have needs to integrate basic data. The integration of technical

and non-technical systems is not common in the branch. Evidence of this is also found in

the widely used dedicated systems and proprietary databases. Also, presently there is a

combination of ways how data is collected.

WG remark: Enterprise Asset Management highlights the need for data-integration. The

questionnaire indicates that many utilities are at the beginning of this integration. The

advice is not to focus on a point-to-point interface between systems, as one can end up

0,68

0,42

0,32

0,79

0,47

0,37

0,47

0,47

0,63

0,21

0,89

0,58

0,42

0,58

0 0,2 0,4 0,6 0,8 1

by-hand

automatic

s tore on paper

s tore digital

planned collec tion

time triggered collec tion

event triggered collec tion

Q19: Now Q20: F uture

Q19/20: How do you collect data and enter it in your systems?


Page 68

with a “spaghetti” of interfaces. Best practice is to focus on aligning the data in the system

landscape based on a common data model and based on good data management. One

should focus for “one single version of the truth” for the most important business objects

(e.g. customer, substation, transformer, outage etc.). The asset manager is the owner of

the process and is in charge to set-up the IT system requirements. This calls for

collaboration between business and IT departments. A variety of technical solutions can

help to integrate on an automation level.

Data processing and data warehousing

The survey covered a subset of questions directed towards finding the current status

regarding processing and warehousing of data (Table 4). Items considered refer to:

Use of decision support systems, analytical and business intelligence systems for

processing of available data, (Q30),

Find if data is merged in data warehouse, and if yes what data is stored there, what

systems do contribute with data and how this is extracted (Q31 – Q35),

0,26

0,42

0,58

0,32

0,68

0,63

0,16

0,11

0,68

0,47

0,21

0,26

0,11

0,26

0,21

0,26

0,16

0,32

0 0,2 0,4 0,6 0,8 1

E nterpris e

R es ourc e P lanning

G eographic

Information S ys tem

E nterpris e A s s et

Management

E nergy

Management

P lanning/s c heduling

s ys tems

MS Offic e

C us tomer

R elations hip

C ontent

Management

Dedic ated, s pec ific

s ys tems

us e now plan to add in the future

Q21-24: What front end systems do you use/plan?


Page 69

Find the current practice regarding the synchronization of data warehouse data with

the source systems like ERP, EAM, GeoIS, CRM etc. (Q36),

The current practices to ensure data quality and for validation of data (Q37).

Question

number

Question

30 Do you use business-intelligence, analytical or decision support systems to

analyse your data?

31 Do you merge data in a data warehouse

32 What subjects are in the data warehouse

33 Is non structured data available for analyses purposes

34 What systems give input for the data warehouse

35 How is the data -needed for these data warehousing and decision support

systems- retrieved from the source systems

36 Do you synchronize data between source systems (SCADA, EAM, ERP, GIS

and CRM)

37 How often is data quality addressed or data validated

Table 0: Questions about data processing

Systems in use

The answers to Q30 to Q33 show that 37% of the respondents use business-intelligence,

analytical or decision support systems to analyse data, whereas 42% have plans on using

those systems in the future, some users have not answered this question.

58% of the respondents are merging data in data warehouse (DWH) and currently 25% are

reported as being operational. The subjects that are met in those data warehouses are

mostly technical (53%) and economic data (58%). With a 10% score the social data is not

well represented. Data warehousing is mostly used for analysing on structured data. Non


Page 70

structured data is only partly available for analysis purposes (47%) and not many

respondents are planning for it. According to the results above data warehousing and

decision support systems are not commonly used in the field of asset management.

0 0.2 0.4 0.6 0.8 1

Yes

No

Q30: Do you use business-intelligence, analytical or desicion support systems to analyze your data?

0 0.2 0.4 0.6 0.8 1

Yes

No

Q31: Do you merge data in a data warehouse

0 0.2 0.4 0.6 0.8 1

For technical data

For economical data

For sociological data

Q32: What subjects are in the data warehouse

0 0.2 0.4 0.6 0.8 1

Yes

Partly

No

Q33: Is non structured data available for analyzes purposes?


Page 71

Data processing from source to data warehouse

The sources used for data warehousing are ERP, EAM, SCADA, GeoIS and CRM (Fig. Q34).

42% of the data coming from these sources is handled with ETL tooling (Automated

Extraction, Transformation and Load into the data warehouse). 32% of the respondents

indicate that this ETL process needs human interference or data is entered by hand (Fig.

Q35).

Synchronizing of data

Respondents (42%) say that only part of the data is synchronized between source systems

(Fig. 36). For the working group this is a key point for data quality. Although a key point

for data quality, the problem is only partly recognized since only 20% plan to improve the

situation.

Q34: What s ys tems give input for the data warehous e?

0,21

0,32

0,21

0,26

0,21

0 0,2 0,4 0,6 0,8 1

S C A DA

E nterpris e As s et Management (E A M)

E nterpris e R es ourc e P lanning (E R P )

G eographic al Information S ys tem

(G IS )

C us tomer R elation Management

S ys tem (C R M)

Q35: How is the data -needed for thes e datawarhous ing and dec is ion

s upport s ys tems - retrieved from the s ource s ys tems ?

0,11

0,21

0,42

0 0,2 0,4 0,6 0,8 1

B y hand

Manualy trans ferred

Automatic ally by

E TL

Q36: Do you s ynchroniz e data between s ourc e s ys tems (S C ADA,

E A M, E R P , G IS and C R M)?

0,05

0,42

0,37

0 0,2 0,4 0,6 0,8 1

Y es

P artly

No

Q37: How often is data quality addres s ed or data validated

0,32

0,42

0,26

0

0,05

0 0,2 0,4 0,6 0,8 1

Daily

Monthly

Y early

L onger

Never


Page 72

However, most respondents indicate that they address data quality or data validation

aspects within a month (graph Q37). Results also show marginal synchronization of data

between sources systems and only some plan for improvement.

WG remark: Beside the synchronization among data sources, in the case of time series

data like from transient recorders, monitoring systems, protection, control system etc., the

time synchronization is important to insure a proper analysis.

Data quality and validation

Since data quality in source systems is an important issue for asset managers the working

group strongly advises to focus on improvement on the quality of data in the source

systems to the appropriate and measurable level. Mastering the most important data is the

key element here. By defining and maintaining “one single version of the truth” and

synchronize this truth all over the source systems data quality will rapidly improve. This

"single version of the truth" also serves as the most important source for data warehouse

and decision support systems since it connects different data (technical and non-technical)

in different source systems.

Data requirements

The survey covered decision and control with a total of 4 questions referring to data

modelling (Q26-Q29) are presented in Table 5.

Question

number

Question

26 Do you have a data model that translated information requirements into data

requirements

27 Does the data model distinguish between the situation of today (AS-IS) and the

future (TO-BE)?

28 Is non-structured data included or used in the data model

29 Do the installed IT systems and applications match with the data model

Table 7: Questions on data requirements


Page 73

The data requirements are derived from information requirements and are made visible in

a data model. Both structured- and unstructured data are present in the utilities. Only in a

few cases unstructured data is modelled in a data model.

When it comes to data modelling of structured data 74% of the respondents answered that

IT systems or applications match at least in part with the data model (Fig. Q29). Also 48%

of the respondents say that unstructured data is part of their data model (Fig. Q28).

WG remark: Data model as used here is a reference model to translate information

requirements from an asset management / enterprise point of view to data requirements

at utility level and not to be understood as final data base model for an application.

Results for the differentiation between technical, economic and social data match the

expectations of the Working Group; there is a strong focus on technical data. The

economic aspects are less represented and data models for social data are not established

(Fig. Q26). Also around one fourth (26%) of the respondents say that they differentiate

between AS-IS and TO-BE data models (Fig. Q27).

WG comment: Results show that enterprise-wide data models exist but that data models

from source systems do not match. This is understandable since suppliers are developing

for a broad range of customers and customer requirements. Data models, once designed,

cannot be easily changed and are a well-kept secret among suppliers due to competition.

Cost of changing data models is high and full of risk in perspective of future releases and

this is not likely to change in the future.

Q26: Do you have a data model that trans lated information

requirements into data requirements ?

0,68

0,26

0,05

0 0,2 0,4 0,6 0,8 1

F or tec hnic al data

F or economic al

data

F or s oc iologic al

data

Q27: Does the data model dis tinguis h between the s ituation of today

(AS -IS ) and the future (TO-B E )?

0,26

0,47

0 0,2 0,4 0,6 0,8 1

Y es

No


Page 74

WG remark: The management should keep full attention to master data and focus on

defining a basic “mean and lean” data model. This master data acts then as an integrator

for your system landscape. The data should not be modelled from the beginning but only

the part that is needed enterprise-wide. Standardization of business objects for “master

data” for main primary and secondary equipment is recommended. This master data

should be organized and managed in one database, as source for all other information

systems.

Information requirements

The survey covered information requirements with one question “how issues of interest for

the asset manager are translated into information specification”. The responses obtained

are shown in the Fig. Q25.

Q28: Is non s tructured data inc luded or us ed in the data model?

0,16

0,32

0,32

0 0,2 0,4 0,6 0,8 1

Y es

P artly

No

Q29: Do the ins talled IT s ys tems and applic ations match with the

data model?

0,16

0,58

0

0 0,2 0,4 0,6 0,8 1

Y es

P artly

No

Q25 Are issues of assetmanagement translated into information specification.

What is the status?

0,11

0,53

0,58

0,79

0,37

0 0,2 0,4 0,6 0,8 1

yes - Enterprise wide

yes - On AM strategy

yes - On risk

assessment/consequences

yes - On maintenance

strategy

yes - On decision and

Control


Page 75

Half of the respondents have translated their asset management issues into information

requirements; the other half has plans to implement this. However, for the ones that

formulate their information requirements only about 11% are doing this on an enterprise-

wide basis. With a score of 79% positive answers, information requirements are formulated

for maintenance strategies. This is followed by a score between 50 and 60% showing that

information requirements are formulated also for AM-strategies and risk assessment.

WG remark: The specification of the information requirements, concerning the needs of

asset management, requires stronger attention, since this is the first step in getting the

ICT-department in the utility to understand the needs of the asset manager. Also, the

absence of a holistic approach is an indication that this area, although important, is not as

mature as recommended. This field of work needs serious attention. From a holistic point

of view invest in this area! A major focus is required on getting and accepting enterprise-

wide basic data definitions for assets and asset events, and information requirements for

the asset management needs. The overall effect will be that different data sources will

match better; hence the overall data quality of your system landscape will rise and reduce

access time and mistakes related to the data. Also, translating the issues of the asset

manager into information specification helps in understanding goals and improving the

necessary cooperation between AM and the IT-department.

Scenario approach and risk balancing

The survey covered scenario approach and risk balancing with 24 questions (Table 6). This

chapter presents the questionnaire results to the following question groups:

Applied philosophy for replacement / renewal strategy, depending on the type of

equipment (Q 38 – Q 46),


(Q 47 – Q 54),

Usage of risk assessment in utilities (Q 55 – Q 62).


Page 76

Question

number

Question

38 What is the applied philosophy for a replacement/renewal strategy,

depending on the type of equipment ... --> this question is

subdivided and continues on following lines

39 .. is it - risk assessment

40 .. is it - condition assessment

41 .. is it - technical life time

42 .. is it - financial life time

43 .. is it - importance (e.g. non delivered energy)

44 .. is it – corrective

45 .. is it – mixed

46 .. is it - nothing, i.e. no strategy

47 What is the applied philosophy for the maintenance strategy,

depending on the type of equipment ... --> this question is

subdivided and continues on following lines

48 .. is it -risk assessment

49 .. is it -condition assessment

50 .. is it -fixed time (time based)

51 .. is it -importance (e.g. non delivered energy)

52 .. is it –corrective

53 .. is it –mixed


Page 77

54 .. is it -nothing/no strategy

55 Do you use the risk assessment? If yes, for which type of equipment?

56 Do you calculate the possible outage costs of equipment?

57 Do you calculate the possible maintenance/replacement costs?

58 Do you take into account for an outage consequences like ... --> this

question is subdivided and continues on following lines

59 .. like economic

60 .. like environmental

61 .. like quality of supply (non-delivered energy)

62 .. like image, public relations

63 Do you use the "risk map" to classify the consequences of an outage in

different types of groups, e.g. moderate, severe, catastrophic?

Table 8: Scenario and Risk

Strategy, depending on the type of equipment

Different strategies are used in utilities depending on the type of equipment, and in most

cases these are mixed strategies. A combination of various basic strategies as can be seen

in the graphs for question 39a/e-46a/e on the applied philosophy for replacement /

renewal strategy by equipment.


Page 78

Q39-46a: What is the applied philos ophy for a replacement/renewal

s trategy on c ircuit breakers

0,42

0,42

0,21

0,11

0,32

0,26

0,74

0

0 0,2 0,4 0,6 0,8 1

ris k as s es s ment

condition as s es s ment

tec hnic al life time

financ ial life time

importance (eg non delivered energy)

c orrective

mixed

nothing, i.e no s trategy

Q39-46b: What is the applied philos ophy for a replacement/renewal

s trategy on pwr. trans formers

0,37

0,37

0,21

0,11

0,32

0,32

0,58

0

0 0,2 0,4 0,6 0,8 1



technic al life time



corrective

mixed


Q39-46c : What is the applied philos ophy for a replacement/renewal

s trategy on cables ?

0,32

0,37

0,16

0,11

0,26

0,21

0,53

0,05

0 0,2 0,4 0,6 0,8 1





importance (e.g. non delivered energy)

corrective

mixed


Q39-46d: What is the applied philos ophy for a replacement/renewal

s trategy on ins tr. Trans formers ?

0,26

0,26

0,32

0,16

0,21

0,32

0,58

0

0 0,2 0,4 0,6 0,8 1






corrective

mixed


Q39-46e: What is the applied philos ophy for a replacement/renewal

s trategy for G IS

0,37

0,32

0,16

0,11

0,32

0,26

0,53

0

0 0,2 0,4 0,6 0,8 1






corrective

mixed



Page 79

The most prominent philosophies applied to the equipment types are summarized as

follows:

Equipment Type Decision Criteria

circuit-breakers: risk / condition assessment

power transformer: risk / condition assessment

cables: condition assessment

instrument transformers: technical lifetime (time-based) / corrective maintenance

gas insulated substation: risk assessment

Table 9: Decision Criteria by Equipment Type

The risk, as well as the condition assessment, is often applied in case of replacement or

renewal strategies whereas the technical lifetime is off minor interest. The exceptions are

the instrument transformers, for which the time-based or the corrective maintenance are

the favourite strategies. The reason is that the condition based maintenance needs an

assessment of the technical condition, which will lead to further financial expenses and

these costs have to be compared to the investment costs of the considered piece of

equipment.


In principle a mixed strategy is often applied by the companies, meaning that different

types of strategies are used for the same asset group during the total useful lifetime of

equipment (Fig. Q48-54). For example, during the first period the time-based

maintenance can be applied and towards the end of the lifetime, a transition to a

condition-based maintenance is useful.


Page 80

Q39-46b: What is the applied philos ophy for a replacement/renewal

s trategy on pwr. trans formers

0 0,2 0,4 0,6 0,8 1






corrective

mixed


Q48-54a: What is the applied philos ophy for the maintenanc e

s trategy on c ircuit breakers

0,32

0,32

0,37

0,21

0,16

0,63

0

0 0,2 0,4 0,6 0,8 1



fixed time (time bas ed)

importance(eg non delivered energy)

corrective

mixed

nothing/no s trategy

Q48-54b: What is the applied philos ophy for the maintenance

s trategy on pwr trans formers

0,32

0,26

0,37

0,16

0,16

0,58

0

0 0,2 0,4 0,6 0,8 1





corrective

mixed


Q48-54c : What is the applied philos ophy for the maintenanc e

s trategy on cables

0,26

0,26

0,26

0,16

0,16

0,42

0

0 0,2 0,4 0,6 0,8 1




importanc e(eg non delivered energy)

correc tive

mixed


Q48-54d: What is the applied philos ophy for the maintenance

s trategy on ins tr. trans formers

0,21

0,21

0,32

0,11

0,05

0,47

0

0 0,2 0,4 0,6 0,8 1





corrective

mixed


Q48-54e: What is the applied philos ophy for the maintenance

s trategy on G IS

0,26

0,32

0,32

0,21

0,11

0,53

0

0 0,2 0,4 0,6 0,8 1





corrective

mixed



Page 81

Also in this case mixed strategies are used. The most prominent philosophies applied to

the equipment depend on the type of equipment:

Equipment Philosophy

circuit-breakers: time-based

power transformer: time-based

cables: risk / condition-based / time-based

instrument transformers: time-based

gas insulated substation: condition-based / time-based

Table 10: Equipment Maintenance Philosophies

The answers show that the importance of the asset in the system influences the choice of

the replacement strategy followed, whereas for the maintenance strategies it is of less

interest.

Use of risk assessment

In general, the philosophy of risk assessment is widely used for the most expensive asset

group, such as power transformer (58 %), GIS (53 %) and circuit-breaker (84 %). For other

equipment risk assessment is not much applied, e.g. for instrument transformers (37 %)

and cables (47 %). This answer is in line with the decision making process in case of

replacement or renewal strategies.

For the risk assessment the overall cost of an outage as well as the cost for maintenance /

replacement to avoid the outage should be taken into consideration too. The responses

show that except in case of power transformers, the costs due to an outage are not widely

considered, whereas considerations of the maintenance/ replacement costs are often used

for all asset groups, mainly for the power transformers, circuit-breakers and GIS (Fig. Q56

- 57).


Page 82

In many cases, the outage consequences (as per question Q58-62) are taken into account

for the decision making process by the utilities and the following point of views are

considered. In any case, the quality of supply plays an important role, followed by

economic aspects:

Economic 95 %

Environmental 74 %

Quality of supply (non-delivered energy) 100 %

Image, public relation 79 %

In theory it makes sense to apply a "risk map" to visualize the risk assessment, because

the risk is defined as a product of probability of an outage and its consequence. But the

answer on corresponding question (Q63 / no figure) show that this type of risk

interpretation is not frequently used (20 %)

Decision and control

The survey covered decision and control aspects with a total of 6 questions (Table 7).

Q56: Do you calc ulate the pos s ible outage cos ts of an equipment?

0,26

0,53

0,26

0,11

0,21

0 0,2 0,4 0,6 0,8 1

c irc uit breaker

power trans formers

cables

ins trument

trans former

G IS

Q57: Do you calc ulate the pos s ible maintenance/replacement

cos ts ?

0,84

0,89

0,53

0,58

0,74

0 0,2 0,4 0,6 0,8 1

c ircuit breaker

power trans formers

cables

ins trument trans former

G IS


Page 83

Question

number

Question

64 Is your decision policy based on an IT support system (in addition to written

reports and demands)

65 Do you apply defined decision criteria

66 Have you defined measurable economic criteria and on what are they based

67 Have you defined measurable technical criteria and on what are they based

68 Have you defined measurable social criteria and on what are they based

69 Have you measurable risk criteria and on what are they based

Table 11: Decision and Control Questions

Decision Policy based on IT support systems

IT systems are at least partly used for decision support in addition to the commonly used

written reports and demands. Over 60% of the respondents indicated that their decision

policies are partly supported using IT systems, whereas only 11% of the respondents

indicate that they do base their decision policy on IT systems (graph Q64). More wide-

ranging is the different criteria used for making decisions, which are somewhat evenly

spread throughout several considerations:

Technical,

Economic,

Condition assessment,

Risk assessment,

Social.


Page 84

It is noted that the decision policy is most commonly based on technical (84%) and

economic (74%) criteria than on other soft-skill considerations such as social (37%) criteria

(Fig. Q65).

Economic criteria

Almost all respondents, who base their decision policy on economic considerations (79% -

Fig. Q65), use economic criteria such as the net present value (NPV) and life cycle cost

(LCC) methodologies beyond the simple consideration of investment costs alone (Fig.

Q66).

Technical criteria

Decision making has historically been based on technical criteria, reflected on the amount

of responses obtained under this category. The respondents were asked to indicate which

from the following specific technical criteria was commonly applied:

Q64: Is your dec is ion polic y bas ed on an IT s upport s ys tem (in

addition to written reports and demands )

0,11

0,21

0,63

0 0,2 0,4 0,6 0,8 1

yes

no

partly

Q65: Do you apply defined dec is ion c riteria

0,74

0,84

0,37

0,63

0,63

0 0,2 0,4 0,6 0,8 1

economic al

technic al

s oc iologic al

condition

as s es s ment


Q66: Have you defined meas urable econimic al c riteria and on what

are they bas ed

0

0,42

0,37

0

0 0,2 0,4 0,6 0,8 1

economic al value

added (E V A)

net pres ent value

(NP V )

minimum life cyc le

cos ts (L C C )

minimum

inves tment cos ts


Page 85

Reliability information,

Condition information,

Age information,

Availability of spare parts,

Availability of know-how.

All of the above mentioned criteria were represented in the survey responses, with a slight

lower percentage for the use of know-how availability (Fig. Q67).

Social criteria

The respondents who consider social criteria in their decision policy mostly include

personal safety and environmental aspects (Fig. Q68): however, it must also be considered

that the measurability of these criteria is highly difficult and subjective and that the

regulatory bodies do have a strong influence in their application.

Q67: Have you defined meas urable tec hnic al c riteria and on what are

they bas ed

0,68

0,74

0,74

0,63

0,37

0,05

0 0,2 0,4 0,6 0,8 1

reliability information

condition information

age information

availability of s pare parts

availability of knowhow

other


Page 86

Risk criteria

Another important criterion refers to the application of risk assessment regarding the

economic, technical and social aspects considered, and the definition and application of

corresponding measurable risk criteria to their own decision policy. From the total of

respondents, who include risk assessment criteria (84% according to 8.1) in their decision

policy, criteria including technical risks are predominantly used (79%), followed by the

inclusion of economic (53%) and social (47%) risks. A consolidation of the risks to define

measurable criteria is not commonly used, being adopted by only 21% of the respondents

(Fig. Q69).

Q68: Have you defined meas urable s oc iologic al c riteria and on what

are they bas ed

0,79

0,58

0,26

0,74

0

0 0,2 0,4 0,6 0,8 1

pers onal s afety

ris ks

equipment s afety

ris ks

power quality

rec laim ris ks

environmental

rec laim ris ks

other

Q69: Have you meas urable ris k c riteria and on what are they bas ed

0,53

0,79

0,47

0,21

0 0,2 0,4 0,6 0,8 1

economic al ris ks

technic al ris ks

s oc ialogic al ris ks

cons olidated ris ks


Page 87

Conclusion

Utilities have a huge amount of raw data available, which needs to be managed to support

the decision making process. Quite a lot of front end data acquisition such as ERP’s

(enterprise resource planning) and EAM’s (enterprise asset management) systems are

available nowadays. From utility point of view to support decision making process all

available IT Systems should be integrated in an overall methodology.


Page 88

FINAL CONCLUSIONS

The report presents an overview of the methodology used for defining IT-strategies for

asset management based on the WG work and results from a survey on this topic. The final

goal of IT-strategies for AM is to obtain better decisions for asset management and for

that it needs to have a holistic point of view including technical, economic and social

aspects. The key aspects to be considered are grouped below:

About integration

The decision-making process requires a holistic approach in the field of replacement,

renewal and maintenance strategies. The results show that the majority of utilities use

mixed strategies, depending on the type of equipment. Decisions are mostly based on

both technical and economic criteria; social criteria such as safety and environmental

aspects are somewhat behind in the IT. But despite the huge amounts of present data

within the utilities and despite the broad range of functionality offered by vendors of

(enterprise) systems, the asset manager still lacks an integrated solution.

About data

To support asset management decisions a huge amount of data is available and the “right

information” should be extracted from it as shown in the leading model, Fig. 1. Primary

equipment is mostly stored in enterprise-wide databases, but this is less common with

secondary equipment. The level of detail of this information is mostly recorded on asset

level. It becomes clear that failure, outage, performance and maintenance data are very

important in the decision making process; this data is well recorded. In addition there is a

high degree of recording diagnosis (offline) data. Most of this data is collected by hand but

this is, with the migration to intelligent grids, definitely going to change according to the

respondents. Still a substantial amount of data is recorded outside the enterprise wide

database. From a data management point of view the utilities should focus on integrating

these existing datasets.


Page 89

About IT-systems

This survey further shows a remarkable need for ERP systems in the utilities and the

integration of basic data as described above. Integration of technical (e.g. SCADA GeoIS,

EAM) and non-technical systems (e.g. CRM, ERP) are not common in the branch. Asset

management strategies urge the need for data- integration and results show that many

utilities are only at the beginning of this integration, starting to buy and to implement

enterprise-wide systems and introducing data warehouse solutions in an attempt to

integrate data. From a utility’s point of view these systems are not sufficient to support the

decision making process since they are not integrated in an overall methodology. That’s

probably the reason why decision support systems are not commonly used in the field of

asset management.

About modelling

According to the leading model (Fig. 1) there is a need to link information requirements

with data requirements. To obtain that link it is necessary to have integrated data and a

well-defined enterprise-wide data model. The survey shows that enterprise-wide data

models do exist but that data models from source systems do not fully match. Source

systems therefore often do not match the requirements of the asset manager. But this is

only part of the gap.

In addition, results also show that existing models, within the utilities themselves, strongly

focus on technical data, less on economic data and social data is only present in some

models. The decision-making process, on the other hand, requires data models that

describe data needed in mixed strategies and matching technical, economic and social

requirements from a holistic point of view. So existing models within the utilities itself

often do not match the needed holistic approach. Succeeding in filling this gap is the first

step in obtaining better decisions in the field of asset management.

In the opinion of the Working Group and based on the responses of the survey it is

important to consider the following items.

From a business perspective:


Page 90

A methodology to evaluate and handle risks should be implemented.

o We hereby like to refer to the Cigré document “Transmission Asset Risk

Management” from Cigré working group C1.16.

Adopt a common information model to be used in the utility.

o Such a model is for example the Common Information Model for Utilities

(CIM) recently adopted by the UCTE. Also the model has to be

adapted/extended to the specific needs of the enterprise, based on needs in

the decision making process (Fig. 15) and to cope with all relevant standards.

Define enterprise wide goals in terms of data quality, data management and data

integration. Information and Communication Technology (ICT) expertise on data

modelling can help here.

Select asset master data based on the reference model.

o Master data is defined as the key data attributes of the most important

business objects.

Initiate Data-Quality processes.

o Start a Data-Quality-Team on a (group of) business object(s). Within that

group, create and maintain master data based on well-defined KPI’s. This

team should implement a process that governs the activities of creating,

updating and deleting master data during the whole lifecycle of a business

object. Best practice indicates that only a limited and dedicated group of

employees should change this master data. Business is responsible for the

content of master data; ICT is responsible for the structure of the master data

From the Information and Communication Technology perspective:

Support this concept of master data with needed functionalities.

Implement software tools to improve data quality and to support the Data-Quality-

Team e.g. by dealing with issues like de-duplication, data cleaning, specialized

input tools, handling of missing and wrong data etc.

As soon as master data functionalities are implemented, extend to other data which

are not part of master data.

Synchronize master data all over the system-landscape.


Page 91

Take the master data source as a reference point. Use this reference point in all

related systems. System by system, and prioritized by the business relevance. This

synchronization varies with the complexity of the system landscape. It is therefore

also a natural incentive to “clean” the system landscape and to improve the quality

of the data.

Organize that every ICT-project and every system-change respect the defined set of

master data.

Its common practice that every ICT-project and every ICT-change or -interface has

to be evaluated before it is implemented. Re-use those processes for

implementation of master data.

By defining, maintaining and synchronizing this set of master data over the entire system

landscape the data quality will rapidly improve. The master data also serves as the most

important reference for use in data warehouse and decision support systems.

An IT strategy for asset management requires a strong partnership between Business and

ICT in a utility. In this partnership business is responsible for the data itself; the contents

and the data quality. ICT is responsible for the structure, tools and availability of data

through the IT-system. This partnership can be enhanced by creating an organisational

unit that facilitates it and even further governance of information throughout the

enterprise. This leads back to the middle of the previous presented leading model (Fig. 1);

it suggests how utilities can form the creation of the intelligent hub.


Page 92

REFERENCES

[1] Cigré JW Group B3/C2 “Maintenance & Reliability”, Task Force 03: Implementation of

information strategy to support utility asset management, Cigré A3&B3 joint colloquium

Tokyo 2005, Paper no. P1-04

[2] Cigré JW Group B3/C2 “Maintenance & Reliability”, Task Force 03: Information Strategy to

support utility asset management, Electra No. 207, April 2003

[3] Cigré Working Group C1.1: Asset Management of Transmission Systems and Associated

Cigré Activities, Cigré Brochure 309, December 2006

[4] Cigré JW Group 23/39.14: Maintenance Outsourcing Guidelines. Cigré Brochure 201,

August 2001

[5] Bartlett, S. (TF23.18): Asset Management in a deregulated environment, Cigré Paris 2002,

report 23-303

[6] Cigré Working Group C1.16: Transmission Asset Risk Management. Cigré Brochure 422,

August 2008

[7] Cigré Working Group 37-27: Ageing of the System – Impact on Planning, Cigré Brochure

176, December 2000

[8] Balzer, G.; Bakic, K.; Haubrich, H.-J., Neumann, C.; Schorn, C.: Selection of an Optimal

Maintenance and Replacement Strategy of H.V. Equipment by a Risk Assessment Process.

Cigré 2006, Paris, rep. B3-103

[9] Working Group C1.11: Asset Management Performance Benchmarking, Cigré Brochure

367, February 2008

[10] Balzer, G.; Schorn, C.: Risk assessment of high voltage equipment. CEPSI 2004, Shanghai,

report 102

[11] Cigré Working Group B3.03: Guidelines to an Optimized Approach to the Renewal of

Existing Air Insulated Substations. Cigré Brochure 300, August 2006

[12] Balzer, G.; Gößmann, T.; Schorn, C.; Benz, T.: The General Asset Management Process of

Power Systems. CEPSI-2008, Macao; report 1052

cigre

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